Evaluation of Demographic Characteristics and Therapeutic Response to ocular Chemical Burn in Patients Referred to Eye Emergency Department of Farshchian Hospital in 2015-2016

Objective:The chemical eye burn is one of the major emergencies in the ophthalmology that will result in irreparable complications in case of inappropriate and timely treatment in severe casesMaterial and Methods:In this cross-sectional study 250 patients with ocular chemical burn who   referred to Farshchian Hospital were enrolled. Demographic characteristic and information regarding the burn were obtained. The Hughes-Roper-Hall classification was used for grading the severity of injury. All patients reevaluated 6 weeks later after injury.Results:Of 250 patients with complete follow up 155 cases ( 62% ) were male and 95 cases ( 38 % ) were female. Chemical injury were more common in the 20-40 years age group ( 108 case = 43/2 % ).The most common cause of chemical injury were occupational injury( 120 case = 48 % ).127 case (50/8 % ) of patients referred during The first hour after injury. The most common material of injury was acid in 102 cases (40/8 % ).Grade I burn was seen in 92 cases ( 36/8 % ) and grade IV in 30 cases ( 12 % ).Grading of the injury was related to the referring time after chemical burn. Severity of injury was more in alkaline burn.In 50 % of grade IV causes the burn had alkaline origin, however only 13/3 % of acid burn had grade IV severity. ( p : 0/001)Conclusion:The incidence of ocular chemical burn was approximately 2/19 % of all patients who referred to emergency ophthalmology service .According to this study ocular injury are more common in the men 20-40 years age group. The most common cause was occupational injury. Delay in referring and alkaline material were poor prognostic factor

Investigating the performance of generative adversarial networks for prostate tissue detection and segmentation

The manual delineation of region of interest (RoI) in 3D magnetic resonance imaging (MRI) of the prostate is time-consuming and subjective. Correct identification of prostate tissue is helpful to define a precise RoI to be used in CAD systems in clinical practice during diagnostic imaging, radiotherapy and monitoring the progress of disease. Conditional GAN (cGAN), cycleGAN and U-Net models and their performances were studied for the detection and segmentation of prostate tissue in 3D multi-parametric MRI scans. These models were trained and evaluated on MRI data from 40 patients with biopsy-proven prostate cancer. Due to the limited amount of available training data, three augmentation schemes were proposed to artificially increase the training samples. These models were tested on a clinical dataset annotated for this study and on a public dataset (PROMISE12). The cGAN model outperformed the U-Net and cycleGAN predictions owing to the inclusion of paired image supervision. Based on our quantitative results, cGAN gained a Dice score of 0.78 and 0.75 on the private and the PROMISE12 public datasets, respectively

Mammographic mass classification using filter response patches

Considering the importance of early diagnosis of breast cancer, a supervised patch‐wise texton‐based approach has been developed for the classification of mass abnormalities in mammograms. The proposed method is based on texture‐based classification of masses in mammograms and does not require segmentation of the mass region. In this approach, patches from filter bank responses are utilised for generating the texton dictionary. The methodology is evaluated on the publicly available Digital Database for Screening Mammography database. Using a naive Bayes classifier, a classification accuracy of 83% with an area under the receiver operating characteristic curve of 0.89 was obtained. Experimental results demonstrated that the patch‐wise texton‐based approach in conjunction with the naive Bayes classifier constructs an efficient and alternative approach for automatic mammographic mass classification